# -*- coding: utf-8 -*-
# __init__.py create by kumiler
# on 2022/11/22 16:46
# desc
from datetime import timedelta
from utils.operators.spark_sql_operator import SparkSqlOperator
from jms.ods.mysql.spmi.spmi_composite_bill import spmi_ods__spmi_composite_bill

spmi_dwd__dwd_spmi_composite_bill_base_dt = SparkSqlOperator(
    task_id='spmi_dwd__dwd_spmi_composite_bill_base_dt',
    task_concurrency=1,
    pool_slots=8,
    master='yarn',
    name='spmi_dwd__dwd_spmi_composite_bill_base_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dwd/spmi/dwd_spmi_composite_bill_base_dt/execute.hql',
    driver_cores=4,
    driver_memory='18G',
    executor_cores=20,
    executor_memory='50G',
    email=['lukunming@jtexpress.com','yl_bigdata@yl-scm.com'],
    num_executors=20,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 50,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 180,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '4G',  # 堆外内存
          'spark.sql.shuffle.partitions': 1600,
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions': 90,  # 每天生成 20 个分区
              'hive.exec.max.dynamic.partitions.pernode': 90,  # 每天生成 20 个分区
              },
    yarn_queue='pro',
    execution_timeout=timedelta(minutes=120),
)


spmi_dwd__dwd_spmi_composite_bill_base_dt << spmi_ods__spmi_composite_bill